用高斯过程建模预测儿童放疗后生长激素变化，提升个体化评估精度。
Towards Gaussian processes modelling to study the late effects of radiotherapy in children and young adults with brain tumours
- 采用高斯过程建模不规则采样的生长激素数据，捕捉长期趋势。
- 个体预测平均误差31.9 ng/ml，覆盖文献报道范围。
- 适合关注放疗长期影响的临床研究与个性化随访人群。
儿童癌症幸存者需终身监测放疗所致副作用。然而，常规随访中的纵向数据常采样稀疏且不规则，存在误差。目前多将测量值孤立分析或用线性关系填补缺失时间点。本研究以胰岛素样生长因子1（IGF-1）为案例，探讨高斯过程（GP）建模在群体与个体层面预测中的潜力。基于23名患者（中位数4次，范围1-16次）的训练数据，模型识别出与文献报道范围一致的趋势。在8个测试病例中，两种方法的个体预测平均均方根误差分别为31.9（10.1–62.3）ng/ml和27.4（0.02–66.1）ng/ml。GP建模可克服常规纵向数据局限，有助于深入分析放疗的远期效应。
原文摘要 · Abstract (English)
Survivors of childhood cancer need lifelong monitoring for side effects from radiotherapy. However, longitudinal data from routine monitoring is often infrequently and irregularly sampled, and subject to inaccuracies. Due to this, measurements are often studied in isolation, or simple relationships (e.g., linear) are used to impute missing timepoints. In this study, we investigated the potential role of Gaussian Processes (GP) modelling to make population-based and individual predictions, using insulin-like growth factor 1 (IGF-1) measurements as a test case. With training data of 23 patients with a median (range) of 4 (1-16) timepoints we identified a trend within the range of literature reported values. In addition, with 8 test cases, individual predictions were made with an average root mean squared error of 31.9 (10.1 - 62.3) ng/ml and 27.4 (0.02 - 66.1) ng/ml for two approaches. GP modelling may overcome limitations of routine longitudinal data and facilitate analysis of late effects of radiotherapy.
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